Loan Application Prediction through machine learning moldes : Logistic Regression, Random Forest, DecisionTree,

Loan Approval Prediction Problem Type Binary Classification Training Accuracy 84% Loan approval prediction is classic problem to learn and apply lots of data analysis techniques to create best classification model. Given with the data set consisting of details of applicants loan and status whether the loan application is approved or not. Basis o…

The project employs Flask-Login for user session management, bcrypt for password hashing, and Flask-Migrate for database migrations. It serves as an example of integrating machine learning functionality within a web application for loan eligibility determination

It is a classification Problem where we are supposed to predict whether a loan would be approved or not.

A simple deep neural network model to predict the approval of personal loan for a person based on features like age, experience, income, locations, family, education, exiting mortgage, credit card etc.

Flask app for predicting loan grant. Model Deployed using Heroku.

This GitHub repository contains a Python script for a machine learning project focused on predicting loan approval using a Support Vector Machine (SVM) classifier.

A loan approval machine learning model that predicts whether a loan request will be approved based on key features such as income, credit score, and employment history. The model was deployed as a web application using Flask, allowing users to input data and receive instant loan approval predictions.

Loan Application Evaluator, using ML approach, Flask and Heroku.

Final project for the Data Science Bootcamp by Digica Academy. This project develops and evaluates a machine learning model to assist in loan approval decisions. Includes business context, data analysis, model selection (Random Forest), performance evaluation, and potential business impact estimation.

Machine Learning Project - Loan Approval Prediction

A Streamlit web application that predicts loan eligibility using machine learning (scikit-learn). Users provide personal and financial details through the web interface, and the app determines the likelihood of loan approval. Built with Python, Streamlit, and a trained model from CSV data.

The goal of this project is to develop a predictive model for loan approval classification by following a comprehensive data science workflow.

This project focuses on predicting loan approval using machine learning algorithms. The model takes various customer features as input and predicts whether a loan application will be approved or not

Developed ML Model to predict whether a loan will be approved or not, based on various parameter, such as Marital Status, Income, etc.

The project aims to predict loan approvals based on various factors, leveraging machine learning models and data pipelines.

End-to-end loan eligibility prediction using XGBoost on Amazon SageMaker with data stored in S3 and tested via local notebook.

Loan approval predictive model using classical classifiers.

Loan default probability prediction system

End-to-end Loan Approval System